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:: Volume 18, Issue 1 (8-2024) ::
JSS 2024, 18(1): 0-0 Back to browse issues page
Bayesian Analysis of Latent Variables in Spatial GLM Models with Stationary Skew Gaussian Random Field
Fatemeh Hosseini * , Omid Karimi
Abstract:   (1268 Views)
The spatial generalized linear mixed models are often used, where the latent variables representing spatial correlations are modeled through a Gaussian random field to model the categorical spatial data. The violation of the Gaussian assumption affects the accuracy of predictions and parameter estimates in these models. In this paper, the spatial generalized linear mixed models are fitted and analyzed by utilizing a stationary skew Gaussian random field and employing an approximate Bayesian approach. The performance of the model and the approximate Bayesian approach is examined through a simulation example, and implementation on an actual data set is presented.
Keywords: Spatial Generalized Linear Mixed Models, Approximate Bayesian Analysis, Gaussian Random Field, Stationary Skew Gaussian Random Field.
Full-Text [PDF 2422 kb]   (610 Downloads)    
Type of Study: Research | Subject: Spatial Statistics
Received: 2023/12/23 | Accepted: 2024/08/31 | Published: 2024/06/4
References
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Hosseini F, Karimi O. Bayesian Analysis of Latent Variables in Spatial GLM Models with Stationary Skew Gaussian Random Field. JSS 2024; 18 (1)
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 18, Issue 1 (8-2024) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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